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Research on Automated Modeling Algorithm Using Association Rules for Traffic Accidents

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ABSTRACT:The study of Influencing factors of traffic accidents is an important research direction in the field of traffic safety. In this paper, the traffic accidents of Shanghai Expressway from April to June 2014 were excavated using association rule mining which generated lots of frequent item sets. The strong rules hidden in these frequent item sets often uncover the association between influencing factors of accidents, which can be used to reduce the occurrence of accidents by breaking them. The rules can also be used to probe usual scenes of accidents, and some corresponding security improvement measures can be taken to prevent the accidents, and ultimately improve the city's traffic safety level. General speaking, association rule mining can produce tons of weak rules, the study first designed a method to calculate minimal Support value of training parameters, and further put forward a way to extract strong rules automatically. The results of the experiments showed that these methods proposed in the paper are effective. Therefore, an automatic modeling algorithm using association rules was finally established to promote the effective application of association rule mining on intelligent transportation system.


Zhen Gao, Ruifeng Pan, Xuesong Wang*, Rongjie Yu. Research on Automated Modeling Algorithm Using Association Rules for Traffic Accidents, 2018 IEEE International Conference on Big Data and Smart Computing, Shanghai, P.R. China, 2018.1.15-18 (Accept).

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